Rosetta-News

Analyse und Vorhersage von Struktur und Faltungsweg (Folding@home, GPUGRID, Rosetta@home, ...)
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Re: Rosetta-News

#253 Ungelesener Beitrag von cappy » 22.05.2009 11:23

hier was aus dem rosetta forum


Feet1st:

Any news on the flu research?

David Baker:

Yes. We already have designed proteins that are predicted in silico to bind tightly to the virus. we next have to test to see if they in fact bind tightly. This requires getting some of the flu surface protein from our collaborators at Scripps, who are preparing it for us. Next, we will synthesize our designed proteins in the laboratory, and then measure their binding to the flu surface protein. The way this all works out experimentally is interesting; perhaps I should post a full description of the experiment in my journal. in any event, which should be getting experimental feedback in a couple of months on our designs.

The difference between what we are doing and the other project you mentioned is that we are designing proteins to bind to the flu virus, wheras they are searching for small molecules which bind to the virus. Both are interesting and important approaches to take to the problem.
und hier eine übersetzung aus unserem forum von Chris_Bear *viele dank nochmal dafür*
Feet1st:

Irgendwas Neues zur Grippe-Forschung?

David Baker:

Ja. Wir haben bereits Proteine entworfen, die sich laut Computersimulationen voraussichtlich eng an den Virus binden. Als nächstes müssen wir testen und sehen ob das wirklich passiert. Dies setzt voraus dass wir etwas Protein der Virusoberfläche von unseren Kollegen bei Scribbs bekommen, die das für uns vorbereiten. Als nächstes werden wir unsere entworfenen Proteine im Labor herstellen, und dann die Bindung an das Virusprotein messen. Es ist interessant wie sich das alles im Experiment entwickeln wird; vielleicht sollte ich eine komplette Beschreibung des Experiments in meinem Blog posten (Amn: "journal" kann hier so ziemlich alles sein, ich nehme einfach mal an dass ein Blog gemeint ist). In jedem Fall sollten wir in ein paar Monaten experimentelle Ergebnisse zu unseren Entwürfen bekommen.

Der Unterschied zwischen dem was wir tun und dem anderen erwähnten Projekt ist dass wir Proteine entwerfen, die sich an das Grippevirus binden, während die anderen nach kleinen Molekülen suchen, die dasselbe tun. Beides sind interessante und wichtige Herangehensweisen um das Problem anzugehen.
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Re: Rosetta-News

#254 Ungelesener Beitrag von cappy » 27.05.2009 04:59

die minirosetta version 1.71 ist raus
minirosetta updated to 1.71. Bug fixes and some local parameter file loading options added in this version.
und ein neuer beitrag aus dem journal
Tonight I will describe how we are going about designing inhibitors for the flu surface protein.

We start from the crystal structure of the key flu virus surface protein and focus on a particular area of the surface of this protein that does not change from strain to strain (as you know, we keep getting the flu because it changes rapidly and new versions can slip by our immune systems because they look different from the versions we fought off in the past). By focusing on an invariant region of the surface we hope to generate broadly neutralizing inhibitors.

The next step in to use Rosetta to dock disembodied amino acid sidechains onto this portion of the virus surface to identify particularly tight low energy interactions. For example, we might find that a tryptophan residue fits in snugly into one pocket on the virus surface, and a tyrosine residue into another. Given these maps of tight binding interactions of isolated amino acids with the virus surface, we then design a protein scaffold which can position as many of these binding "hotspot" residues as possible with orientations correct for binding the virus simultaneously. The next step is to optimize the remaining residues of the protein we are designing to interact as tightly as possible with the virus.

We plan to experimentally test around 20 of the novel proteins generated using the above procedure. We will of course pick the designs which have the strongest computed interactions with the virus. We will experimentally test more than 1 design because our computational design methods, while powerful, are still not perfect, so we don't expect every design predicted to bind tightly to actually bind tightly.

To experimentally test the designed inhibitors, we begin by synthesizing artificial genes encoding the new proteins. We simply use the genetic code in reverse: for each amino acid in the computer generated protein sequence we insert the appropriate DNA base "codon" until we have covered the whole sequence. Once we have the synthetic genes, we carry out two types of experimental tests. I'll describe these in my next post.
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Re: Rosetta-News

#255 Ungelesener Beitrag von DersaureFritz » 27.05.2009 08:49

Und jetzt noch die Übersetzung! :o
Gib dir mal Mühe! :D

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Michael H.W. Weber
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Re: Rosetta-News

#256 Ungelesener Beitrag von Michael H.W. Weber » 27.05.2009 10:10

...und hier dann die Übersetzung:
Tonight I will describe how we are going about designing inhibitors for the flu surface protein.

We start from the crystal structure of the key flu virus surface protein and focus on a particular area of the surface of this protein that does not change from strain to strain (as you know, we keep getting the flu because it changes rapidly and new versions can slip by our immune systems because they look different from the versions we fought off in the past). By focusing on an invariant region of the surface we hope to generate broadly neutralizing inhibitors.

The next step in to use Rosetta to dock disembodied amino acid sidechains onto this portion of the virus surface to identify particularly tight low energy interactions. For example, we might find that a tryptophan residue fits in snugly into one pocket on the virus surface, and a tyrosine residue into another. Given these maps of tight binding interactions of isolated amino acids with the virus surface, we then design a protein scaffold which can position as many of these binding "hotspot" residues as possible with orientations correct for binding the virus simultaneously. The next step is to optimize the remaining residues of the protein we are designing to interact as tightly as possible with the virus.

We plan to experimentally test around 20 of the novel proteins generated using the above procedure. We will of course pick the designs which have the strongest computed interactions with the virus. We will experimentally test more than 1 design because our computational design methods, while powerful, are still not perfect, so we don't expect every design predicted to bind tightly to actually bind tightly.

To experimentally test the designed inhibitors, we begin by synthesizing artificial genes encoding the new proteins. We simply use the genetic code in reverse: for each amino acid in the computer generated protein sequence we insert the appropriate DNA base "codon" until we have covered the whole sequence. Once we have the synthetic genes, we carry out two types of experimental tests. I'll describe these in my next post.
Heute Nacht werde ich beschreiben, wie wir bei dem Design von Inhibitoren für das Grippe-Oberflächenprotein vorgehen.

Wir gehen aus von der Kristallstruktur des viralen Schlüsseloberflächenproteins und konzentrieren uns auf eine bestimmte Region auf der Oberfläche dieses Proteins, die sich von Virusstamm zu Virusstamm nicht ändert (wie ihr wißt bekommen wir immer wieder Grippe, weil sie sich rasch verändert und die neuen Varianten unserem Immunsystem entkommen, weil sie anders aussehen). Durch die Auswahl eines invarianten Bereichs hoffen wir allgemein anwendbare Inhibitoren erzeugen zu können.

Der nächste Schritt besteht darin, Aminosäureseitenketten auf diesen Bereich des Virusoberflächenproteins zu docken, um starke Bindungen mit niedrigem Energiegehalt zu identifizieren. Z.B. könnten wir finden, daß ein Tryptophanrest genau in eine Virusoberflächenbindungstasche paßt und ein Tyrosinrest in eine andere. Sobald wir eine Karte haben, welche isolierten Aminosäureseitenketten wo binden, können wir damit ein Protein designen, welches die erforderlichen Bindeaminosäuren möglichst in Kombination genau so positioniert, wie wir es wünschen. Danach folgt eine Optimierung der nichtbindenden Aminosäurerest des Designerproteins, sodaß insgesamt maximale Bindungsaffinität erreicht wird.

Wir planen, etwa 20 der nach oben beschriebenem Schema erzeugten neuen Designerproteine experimentell zu testen, da wir zwar von den Möglichkeiten unserer Methode überzeugt sind, aber eben nicht sichergestellt ist, daß ein einziges Designerprotein bereits gut genug ist, um die Aufgabe zu erfüllen.

Um die experimentell zu testenden Inhibitoren herstellen zu können, synthetisieren wir künstliche Gene, die genau für die Aminosequenz der Designerproteine kodieren. Sobald wir diese synthetischen Gene haben, führen wir zwei Sorten von experimentellen Tests durch, die ich in meiner nächsten Mitteilung beschreiben werde.

Michael.
Fördern, kooperieren und konstruieren statt fordern, konkurrieren und konsumieren.

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Re: Rosetta-News

#257 Ungelesener Beitrag von DersaureFritz » 28.05.2009 07:23

Danke schön Michael! :good:

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Re: Rosetta-News

#258 Ungelesener Beitrag von cappy » 12.06.2009 04:57

die minirosetta version 1.75 ist draussen
This one includes a number of new features allowing us to analyse larger amounts of data for homolog modelling and has new features for Docking, and, as always, bugfixes and (hopefully) improved stability.
----


die minirosetta version 1.76 ist draussen
This is a minor update to fix the problems with validation.
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Re: Rosetta-News

#259 Ungelesener Beitrag von cappy » 23.06.2009 05:04

es gibt nun die minirosetta version 1.80
In this version:
New protein-protein docking protocol.
New rotamer library.
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Re: Rosetta-News

#260 Ungelesener Beitrag von cappy » 03.07.2009 13:59

was neues aus dem journal
Manuel asked in the discussion thread about the manuscripts I described a couple of months ago. The second manuscript has already been published in a journal with the excellent policy of making all articles free to the public, you can take a look at

http://www.pnas.org/content/106/23/9215.long

The first manuscript was just accepted for publication in the same journal (proceedings of the national academy of sciences). The third and fourth manuscripts have also been accepted but not yet published.

The sixth manuscript got very enthusiastic reviews in the widely read journal Nature and hopefully you will be able to read about it there in not too long.

While we are all most interested in developing cures for diseases, aging and other problems, scientific papers are still a good way of benchmarking progress towards these goals. Your contributions to rosetta@home are certainly having a big impact on this short term measure of progress, and we will keep working together towards the longer term goals!
der artikel von dem link ist recht lang vielleicht kann ja einer in kurz fassung schreiben was da drin steht :)
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Re: Rosetta-News

#261 Ungelesener Beitrag von cappy » 15.07.2009 17:55

neue minirosetta version 1.82

das changelog
- Support for Disulfides. New Science!
- Bug fixes for homology modelling with constraints
- SOme bugfixes in the watchdog
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Re: Rosetta-News

#262 Ungelesener Beitrag von cappy » 25.07.2009 07:56

was neues aus dem journal :)
Many of you know that David Kim is the architect of Rosetta@home and spends much of his time maintaining and improving rosetta@home. He also has had time to carry out some very interesting research. Some time ago (before the papers I described in my posts below were submitted), David wrote a paper describing his results on the search problem in protein structure prediction, and submitted it for publication. In contrast to much of our work, which is on biomedical problems such as aging as described below, David's work described in the paper is on the fundamental question of what limits our ability to predict structures accurately for larger proteins. We received the reviews from anonymous referees recently, and to give you some insight into what the process of scientific publishing is like, I'm pasting first the abstract and then the reviews here:

abstract of David Kim's paper:
The primary obstacle to de novo protein structure prediction is conformational sampling: the native state generally has lower free energy than non-native structures but is exceedingly difficult to locate. Structure predictions with atomic level accuracy have been made for small proteins using the Rosetta structure prediction methodology, but for larger and more complex proteins, the native state is virtually never sampled and it has been unclear how much of an increase in computing power would be required to successfully predict the structures of such proteins. In this paper we develop an approach to determining how much computer power is required to accurately predict the structure of a protein, based on a reformulation of the conformational search problem as a combinatorial sampling problem in a discrete feature space. We find that conformational sampling for many proteins is limited by critical ?linchpin? features, often the backbone torsion angles of individual residues, which are sampled very rarely in unbiased trajectories and when constrained dramatically increase the sampling of the native state. These critical features frequently occur in less regular and likely strained regions of proteins that contribute to protein function. In a number of proteins, the linchpin features are in regions found experimentally to form late in folding, suggesting a correspondence between folding in silico and in reality.

Anonymous reviews of David's paper (we got these about three months after the paper was submitted to the journal).

Reviewer #1: It's an excellent and interesting paper. I certainly recommend publication. The novelty here is the idea of feature strings from native proteins and the identification by Bayesian analysis of "linchpin features" that are hard to find computationally and may be slow to develop physically.

I recommend publication, as is, but I would encourage the authors to comment on how to understand their linchpin features. For example, why should they reside near functional sites? One might have expected biological function not to have a role in determining the folding difficulty and kinetics. It would be interesting to hear the authors' thoughts about why these particular linchpins?

Reviewer #2: Ref: JMB-D-09-00307
Title: Sampling bottlenecks in de novo protein structure prediction
Authors: David E Kim; Ben Blum; Philip Bradley; David Baker

This is a provocative and important paper, and overall is handled very nicely with helpfully detailed examples. Quantifying an estimate of required sampling times and demonstrating that a feature list is adequate are both valuable, and the identification of unfavorable "linchpin" features that stall the predictions seems likely to be a very pivotal observation. It's also a nice name for them. I definitely recommend publication.

************* end of reviews

The paper has now been accepted, and will appear in the journal of molecular biology which I believe can be freely accessed by anyone. now you know what David was doing in his spare time last year; he has now switched to working on another project I will tell you about some other time.
wäre schön wenn einer zait findet das zu übersetzen ;)
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Re: Rosetta-News

#263 Ungelesener Beitrag von laguna » 25.07.2009 08:08

Viele von Ihnen wissen, dass David Kim der Architekt von Rosetta@home ist und viel seiner Zeit damit verbringt rosetta@home zu pflegen und zu verbessern. Er auch Zeit gehabt, eine sehr interessante Forschung durchzuführen. Vor einiger Zeit (bevor die Papiere, die ich in meinen folgenden Posts beschrieb, eingereicht wurden), schrieb David ein Papier, das seine Resultate betreffend das Suchproblem in der Proteinstrukturvorhersage beschreibt und reichte es zur Publikation ein. Im Gegensatz zu viel unserer Arbeit, bei der es um biomedizinische Probleme wie das Altern geht, wie unten beschrieben, beschäftigt sich Davids Arbeit, die im Papier beschrieben ist, mit der grundlegenden Frage, welcher Begrenzungen unsere Fähigkeit, Strukturen für größere Proteine genau vorauszusagen unterworfen ist. Wir bekamen die ersten Berichte von den anonymen Referenten vor kurzem und um etwas Einblick in den Prozess des wissenschaftlichen Veröffentlichens zu geben in, poste ich hier zuerst die Zusammenfassung und dann die Berichte:

Tja uns da verließen ihn die Fachausdrücke aus Chemie/Physik/Biologie...

Vielleicht versucht sich ja jemand mit Fachwissen an dem Abstract. :oops:
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Re: Rosetta-News

#264 Ungelesener Beitrag von cappy » 16.08.2009 20:23

was neues aus dem journal, kann das vllt jemand übersetzen bitte?

We just learned that a model we had generated using the "fold and dock" method on your computers allowed the solution of the phase problem for structural biologists who had grown crystals of the (dimeric) protein but not been able to solve its structure--this result led to a huge flurry of email today as the approach could be very useful for a broad class of problems.

As I mentioned earlier, the paper on the "fold and dock" protocol (which you can recognize by the symmetric dancing of molecules on your screensaver) was enthusiastically accepted for publication in the proceedings of the national academy of sciences and will be free on line soon. Here is one of the anonymous reviews of the manuscript:

"The authors introduce a simultaneous "fold and dock" routine for the prediction of symmetric systems with impressive results. The method combines the Rosetta folding algorithm and the lab's more recent symmetric docking protocol. Here, both the backbone moves and relative positioning of the components are accomplished in the same Monte Carlo routine. Undoubtedly, this feature is necessary for most of the systems, particularly those which are intertwined. The authors both benchmark their results and even include a few predictions (using structures that came out after simulations). In addition they test the utility of the method for ab initio phasing and structure prediction incorporating some NMR-based constraints. This is an extremely nice piece of work which addresses an outstanding challenge in structural biology and protein structure prediction."
und
As Mod Sense very nicely described in the discussion thread, with rosetta@home we are tackling basic research problems in addition to disease related research such as the gene therapy efforts I described in my last post and the amyloid fiber blocking and other projects described in the "disease related research" section on our home page. One of the "basic research" problems we are tackling with rosetta@home is the development of more accurate energy functions. This is a critical problem for design of new drugs to cure diseases. Here is why:

Almost all drugs work by binding tightly to a protein structure and blocking or modulating the function of the protein. To be produced cheaply, and to be able to access the inside and outside of your cells, it is usually desirable for a drug to be a not too large molecule. Chemists have put together large collections of potential drug molecules both in computer databases and in actuality. Big pharmaceutical companies have on the order of millions of such compounds they can test.

So--you would think that to find a new drug, given the structure of a protein target, one could simply screen on the computer for the drug that binds the tightest to the target. For each potential drug compound, we can determine its lowest energy binding mode to the target using Rosetta docking--you have probably seen small molecules docking into proteins on your screensavers. There are other programs for small molecule docking as well which are used in other distributed computing projects.

We and others have been pretty successful at predicting how a specific small molecule binds to a protein. We can test this by comparing the lowest energy structure you find on rosetta@home with the crystal structure of the small molecule bound to the protein if there is one. the big advantage of rosetta compared to other approaches is that the protein can flex during the docking process; this is important because many experimentally determined structures have shown considerable movements take place in proteins when they bind small molecules.

However, for drug discovery, the problem is not just to dock one small molecule into a protein, but to dock millions of small molecules into proteins, and find the one that binds the tightest. the amount of computer time required to accurately dock millions of small molecules into a protein target with a detailed physically realistic model like Rosetta is too large for rosetta@home currently. however, this is not the only problem. different small molecules have different numbers and different types of atoms, and determining which of these has the tightest binding energy to the protein is very challenging. We and others have had considerable difficulty in ranking sets of compounds known to bind a target, because errors in energy computation have big effects. We can predict protein structures accurately because the correct structure has very much lower energy than any other structure, but relatively small errors in energy computation can drastically change the ranking of different small molecules bound to a protein.

Because of these problems, large pharmaceutical companies surprisingly enough rely as much or more on brute force experimental screening to see which of their millions of compounds bind most tightly to a target as they do on screening on the computer. of course, screening through millions of compounds for the tightest binder experimentally is very slow and hugely expensive-this is one of the reasons why drug discovery is so expensive.

So if we can improve our ability to calculate energies accurately, it would have a huge effect on many important applications, including drug discovery and design. In my next post, I'll explain how we are using rosetta@home to increase the accuracy of energy calculations.
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